However, the input point cloud size is set to 4096 points.
We use the Adam optimizer [1]} with a learning rate of \(1e-3\)  and a weight decay of \(1e-5\) .
The batch size is set to 20 and we train the network for 500 epochs.
For inference, we do not feed the entire input to the network.
We create patches of input point clouds and process each patch individually.
Merging all upsampled patches results in very large number of points in the final result and points may be too closer in the overlapping region.
Therefore, we use farthest point sampling to sample the merged point cloud patches to exactly four times of the size of the input point cloud.
